Modulation recognition algorithm based on mixed attention prototype network

基于混合注意力原型网络的调制识别算法

Information and Navigation College, Air Force Engineering University, Xi'an 710077, China

Received: 11 March 2022

Abstract

针对极少量带标签样本条件下的通信信号调制识别难题, 提出一种基于混合注意力原型网络的调制识别算法。综合元学习和度量学习的思想, 在原型网络框架下通过特征提取模块将信号映射至一个新的特征度量空间, 并通过比较该空间内各类原型与查询信号之间的距离确定查询信号调制样式。根据通信信号IQ分量的时序特点设计了由卷积神经网络和长短时记忆网络级联的特征提取模块, 并引入卷积注意力机制提升关键特征的权重; 采用基于Episode的训练策略, 使算法可泛化到新的信号识别任务中。仿真结果表明, 所提算法在每类信号只有5个带标签样本(5-way 5-shot)时平均识别率可达85.68%。

摘要

A modulation recognition algorithm based on mixed attention prototype network is proposed to solve the modulation recognition problem of communication signals with very few labeled samples. Combining the ideas of meta learning and metric learning, the algorithm maps the signal to a new feature metric space by feature extraction module in the prototype network framework and determines the modulation pattern of the query signal by comparing the distance between each prototype and the query signal in the space. According to the time sequence characteristics of communication signal IQ components, a feature extraction module is designed which is cascated by the convolutional neural network and long-short term memory network, and the convolutional attention mechanism is introduced to improve the weight of key features. The training strategy based on Episode is used to generalize the algorithm to new signal recognition tasks. The simulation results show that the average recognition rate of the present algorithm can reach 85.68% when there are only 5 labeled samples (5-way 5-shot) for each type of signal.